R. A. Sukamto, M. Rischa, E. Piantari, Yudi Wibisono, R. Megasari
Comments in source code are a form of inline documentation created by programmers to help others understand the function of the program. The students of the basic programming subject need how to learn to write better code comments which can be difficulties for the lecturer assessing. Therefore, the author proposes an automatic source code comment assessment method for the online judge system with a corpus-based text similarity approach. Word2vec, GloVe, and fastText models will be used to train word vectors with the Indonesian Wikipedia Dump. The Similarities will be measured using Word Mover's Distance (WMD). Experiments were carried out using epoch variations during the training process. Spearman's rho correlation coefficient, mean average error (MAE), and performance measurements of each model will be compared. The methods with the proposed word embedding approach still provide not good results.
{"title":"Auto Code Comment Assessment for Online Judge using Word Embedding and Word Mover's Distance","authors":"R. A. Sukamto, M. Rischa, E. Piantari, Yudi Wibisono, R. Megasari","doi":"10.1145/3575882.3575949","DOIUrl":"https://doi.org/10.1145/3575882.3575949","url":null,"abstract":"Comments in source code are a form of inline documentation created by programmers to help others understand the function of the program. The students of the basic programming subject need how to learn to write better code comments which can be difficulties for the lecturer assessing. Therefore, the author proposes an automatic source code comment assessment method for the online judge system with a corpus-based text similarity approach. Word2vec, GloVe, and fastText models will be used to train word vectors with the Indonesian Wikipedia Dump. The Similarities will be measured using Word Mover's Distance (WMD). Experiments were carried out using epoch variations during the training process. Spearman's rho correlation coefficient, mean average error (MAE), and performance measurements of each model will be compared. The methods with the proposed word embedding approach still provide not good results.","PeriodicalId":367340,"journal":{"name":"Proceedings of the 2022 International Conference on Computer, Control, Informatics and Its Applications","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121346620","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Huzaifi Hafizhahullah, A. R. Yuliani, H. Pardede, A. Ramdan, Vicky Zilvan, Dikdik Krisnandi, Jimmy Kadar
The capacity degradation of battery can occur due to continuously used as primary energy source equipment. An accurate prediction of battery remaining useful life (RUL) is necessary to avoid system functionality failure. This study proposes battery RUL prediction using data-driven method based on a hybrid deep model of Convolutional Neural Networks (CNN) and Long-Short Term Memory (LSTM). CNN and LSTM are used to extract features from multiple measurable data in parallel. CNN extracts features of multi-channel charging profiles, whereas LSTM extracts features of historical capacity data of discharging profiles which related to time dependency. An error index is compared between single model LSTM and hybrid model CNN-LSTM. The result indicates that the proposed hybrid model outperforms the single model by up to 37%-61% in case of mean absolute percentage error.
{"title":"A Hybrid CNN-LSTM for Battery Remaining Useful Life Prediction with Charging Profiles Data","authors":"Huzaifi Hafizhahullah, A. R. Yuliani, H. Pardede, A. Ramdan, Vicky Zilvan, Dikdik Krisnandi, Jimmy Kadar","doi":"10.1145/3575882.3575903","DOIUrl":"https://doi.org/10.1145/3575882.3575903","url":null,"abstract":"The capacity degradation of battery can occur due to continuously used as primary energy source equipment. An accurate prediction of battery remaining useful life (RUL) is necessary to avoid system functionality failure. This study proposes battery RUL prediction using data-driven method based on a hybrid deep model of Convolutional Neural Networks (CNN) and Long-Short Term Memory (LSTM). CNN and LSTM are used to extract features from multiple measurable data in parallel. CNN extracts features of multi-channel charging profiles, whereas LSTM extracts features of historical capacity data of discharging profiles which related to time dependency. An error index is compared between single model LSTM and hybrid model CNN-LSTM. The result indicates that the proposed hybrid model outperforms the single model by up to 37%-61% in case of mean absolute percentage error.","PeriodicalId":367340,"journal":{"name":"Proceedings of the 2022 International Conference on Computer, Control, Informatics and Its Applications","volume":"85 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116524014","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A. Rozie, P. Khotimah, Andria Arisal, Lia Sadita, M. H. Izzaturrahim
One of the contents that will play an important role in the trip planning system is content related to geospatial. To ensure that the trip planning system is optimal and safe, information about traffic conditions, events, as well as their location is required. People often talk about traffic conditions on social media, such as traffic jams, detours, or accidents. These activities provide textual data related to traffic events. Location is regarded as essential information from traffic event-related texts to identify where the event took place. This study uses Indonesian short text for location extraction with named entity recognition (NER) technique. Data from twitter-based social media (lewatmana.com) are collected. Bidirectional Long Short-Term Memory - Conditional Random Field (BiLSTM - CRF) model and Indonesian POS tagger are used to develop the named entity recognition model for location extraction. Our current model shows promising results with 91.21% accuracy.
{"title":"Location extraction from Traffic Event-related Text","authors":"A. Rozie, P. Khotimah, Andria Arisal, Lia Sadita, M. H. Izzaturrahim","doi":"10.1145/3575882.3575946","DOIUrl":"https://doi.org/10.1145/3575882.3575946","url":null,"abstract":"One of the contents that will play an important role in the trip planning system is content related to geospatial. To ensure that the trip planning system is optimal and safe, information about traffic conditions, events, as well as their location is required. People often talk about traffic conditions on social media, such as traffic jams, detours, or accidents. These activities provide textual data related to traffic events. Location is regarded as essential information from traffic event-related texts to identify where the event took place. This study uses Indonesian short text for location extraction with named entity recognition (NER) technique. Data from twitter-based social media (lewatmana.com) are collected. Bidirectional Long Short-Term Memory - Conditional Random Field (BiLSTM - CRF) model and Indonesian POS tagger are used to develop the named entity recognition model for location extraction. Our current model shows promising results with 91.21% accuracy.","PeriodicalId":367340,"journal":{"name":"Proceedings of the 2022 International Conference on Computer, Control, Informatics and Its Applications","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121915563","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Ayu Shabrina, Irma Palupi, Bambang Ari Wahyudi, I. Wahyuni, Mulya Diana Murti, A. Latifah
Carbon emissions produced by forest fires contribute to the global emission increase. The amount of carbon emission may indicate the severity of the fires. In a dry climate condition, forest fires become an unexpected serious problem. This paper investigates the effect of climate variables on forest fires in Sumatra from 1998 to 2018. We employ two methods, Random Forest (RF) and Artificial Neural Network (ANN) to predict the carbon emission in 2019-2021. The total emission over the domain and the fire distribution map are compared in both models. As a result, the RF model is more accurate in predicting the location and intensity in 2019 but overestimates in 2020-2021. This indicates that the RF model gives a slightly better prediction when the carbon emission is high. This result is consistent with the evaluation metrics showing that ANN mostly gives smaller errors. Also, we found that the climate variables are still relevant to describe the carbon emissions through both models with importance scores of more than .
{"title":"Modelling the climate factors affecting forest fire in Sumatra using Random Forest and Artificial Neural Network","authors":"Ayu Shabrina, Irma Palupi, Bambang Ari Wahyudi, I. Wahyuni, Mulya Diana Murti, A. Latifah","doi":"10.1145/3575882.3575920","DOIUrl":"https://doi.org/10.1145/3575882.3575920","url":null,"abstract":"Carbon emissions produced by forest fires contribute to the global emission increase. The amount of carbon emission may indicate the severity of the fires. In a dry climate condition, forest fires become an unexpected serious problem. This paper investigates the effect of climate variables on forest fires in Sumatra from 1998 to 2018. We employ two methods, Random Forest (RF) and Artificial Neural Network (ANN) to predict the carbon emission in 2019-2021. The total emission over the domain and the fire distribution map are compared in both models. As a result, the RF model is more accurate in predicting the location and intensity in 2019 but overestimates in 2020-2021. This indicates that the RF model gives a slightly better prediction when the carbon emission is high. This result is consistent with the evaluation metrics showing that ANN mostly gives smaller errors. Also, we found that the climate variables are still relevant to describe the carbon emissions through both models with importance scores of more than .","PeriodicalId":367340,"journal":{"name":"Proceedings of the 2022 International Conference on Computer, Control, Informatics and Its Applications","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114324110","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A. Nisa, Radhiyatul Fajri, Erwin Nashrullah, Fandy Harahap, Junanto Prihantoro, G. Wibowanto, Jemie Muliadi, Anto Nugroho
The Face Recognition system has a challenge when the conditions of the input images have differences in quality from the images that have been enrolled in the database. One of the causes is the variation in lighting that causes illumination in image. We used images with normal lighting, as well as four images that have variations in the lighting/illumination directions. Face Image Quality Assessment (FIQA) helps the face recognition system to ensure the optimum captured image quality for enrollment and verification process. We use both supervised (FaceQnet) and unsupervised (SDD-FIQA, SER-FIQ) FIQA method against the Asian Face Image dataset. The result shows that filtering images using FIQA method can reduce FNMR by 58.89% in matching images whose light direction is from below. Images with type 2 illumination, where an image whose light comes from below matched with normal image, gave the lowest result in FRR compared to other types of illumination when tested with 3 FIQA methods.
{"title":"Performance Face Image Quality Assessment under the Difference of Illumination Directions in Face Recognition System using FaceQnet, SDD-FIQA, and SER-FIQ","authors":"A. Nisa, Radhiyatul Fajri, Erwin Nashrullah, Fandy Harahap, Junanto Prihantoro, G. Wibowanto, Jemie Muliadi, Anto Nugroho","doi":"10.1145/3575882.3575924","DOIUrl":"https://doi.org/10.1145/3575882.3575924","url":null,"abstract":"The Face Recognition system has a challenge when the conditions of the input images have differences in quality from the images that have been enrolled in the database. One of the causes is the variation in lighting that causes illumination in image. We used images with normal lighting, as well as four images that have variations in the lighting/illumination directions. Face Image Quality Assessment (FIQA) helps the face recognition system to ensure the optimum captured image quality for enrollment and verification process. We use both supervised (FaceQnet) and unsupervised (SDD-FIQA, SER-FIQ) FIQA method against the Asian Face Image dataset. The result shows that filtering images using FIQA method can reduce FNMR by 58.89% in matching images whose light direction is from below. Images with type 2 illumination, where an image whose light comes from below matched with normal image, gave the lowest result in FRR compared to other types of illumination when tested with 3 FIQA methods.","PeriodicalId":367340,"journal":{"name":"Proceedings of the 2022 International Conference on Computer, Control, Informatics and Its Applications","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127714054","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Ridwan Suhud, M. Hanif, Christoporus Deo Putratama, K. Prakoso, Bramantio Yuwono, A. Prihatmanto
The earthquake monitoring system is a system that provides the latest earthquake information detected by seismic sensors ECN based on an accelerometer which has been developed independently in previous studies. This study uses ECN sensors to process and analyze peak ground acceleration (PGA), earthquake intensity, and visualization acceleration graphs in three axes (x, y, z) through the visualization on the website. This research includes sending ECN sensor data to the message broker server and then taking data (consume) to the database server and processing and analyzing data from the database into helpful information for users. The system design results are a prototype of a web-based application that displays the latest news on the state of the earthquake in the form of peak ground acceleration and earthquake intensity, as well as visualization through maps from the sensor location.
{"title":"Designing Earthquake Monitoring System Using Earthquake Catcher Network","authors":"Ridwan Suhud, M. Hanif, Christoporus Deo Putratama, K. Prakoso, Bramantio Yuwono, A. Prihatmanto","doi":"10.1145/3575882.3575888","DOIUrl":"https://doi.org/10.1145/3575882.3575888","url":null,"abstract":"The earthquake monitoring system is a system that provides the latest earthquake information detected by seismic sensors ECN based on an accelerometer which has been developed independently in previous studies. This study uses ECN sensors to process and analyze peak ground acceleration (PGA), earthquake intensity, and visualization acceleration graphs in three axes (x, y, z) through the visualization on the website. This research includes sending ECN sensor data to the message broker server and then taking data (consume) to the database server and processing and analyzing data from the database into helpful information for users. The system design results are a prototype of a web-based application that displays the latest news on the state of the earthquake in the form of peak ground acceleration and earthquake intensity, as well as visualization through maps from the sensor location.","PeriodicalId":367340,"journal":{"name":"Proceedings of the 2022 International Conference on Computer, Control, Informatics and Its Applications","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134190706","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Hanif Fakhrurroja, Ahmad Musnansyah, Muhammad Dewan Satriakamal, Bima Kusuma Wardana, Rizal Kusuma Putra, Dita Pramesti
This research discusses how to interact with a smart home using speech recognition and a touchscreen to control electronic devices. Google Speech Cloud API use to process speech-to-text and text-to-speech. The system is built in a mobile-based application using a touchscreen as remote control and speech to control the electronic devices. This mobile application is made using the Flutter framework. We use natural language understanding (NLU) in speech processing to determine the intent. The learning process in a dialogue system is based on reinforcement learning. Interaction through the touch screen on the mobile application performs well, while the dialogue system based on reinforcement learning accuracy rate is 83.33%.
{"title":"Dialogue System based on Reinforcement Learning in Smart Home Application","authors":"Hanif Fakhrurroja, Ahmad Musnansyah, Muhammad Dewan Satriakamal, Bima Kusuma Wardana, Rizal Kusuma Putra, Dita Pramesti","doi":"10.1145/3575882.3575911","DOIUrl":"https://doi.org/10.1145/3575882.3575911","url":null,"abstract":"This research discusses how to interact with a smart home using speech recognition and a touchscreen to control electronic devices. Google Speech Cloud API use to process speech-to-text and text-to-speech. The system is built in a mobile-based application using a touchscreen as remote control and speech to control the electronic devices. This mobile application is made using the Flutter framework. We use natural language understanding (NLU) in speech processing to determine the intent. The learning process in a dialogue system is based on reinforcement learning. Interaction through the touch screen on the mobile application performs well, while the dialogue system based on reinforcement learning accuracy rate is 83.33%.","PeriodicalId":367340,"journal":{"name":"Proceedings of the 2022 International Conference on Computer, Control, Informatics and Its Applications","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129801976","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Stereo vision finds a wide range of applications for robot navigation, advanced driving support system, and autonomous driving in the automotive industry. The disparity map can be obtained through the implementation of stereo vision architecture using stereo matching. A stereo matching algorithm has recently been executed in FPGA. This study is aimed at assessing the stereo matching with the use of Stratix V FPGA and OpenCL framework. The latter refers to a parallel programming framework that enhances productivity by raising the code’s abstraction. Additionally, OpenCL allows for the processing of stereo matching using channel extensions. In the experiment, we partitioned the OpenCL kernel into three smaller kernels to examine the stereo matching on FPGA for computation. Such an approach enables streaming image pixels from the FPGA global memory. A line-buffer is employed to avoid the load-store dependencies caused by memory accesses when streaming the pixels to the window buffer inside the stereo matching kernel. We can achieve a rapid execution time, which is advantageous for real-time implementation, by streaming the image pixels through an OpenCL kernel partitioned using channel extension. The execution time to compute the disparity map using the stereo KITTI dataset with 1242x375 pixels resolution reaches 2.38 ms or 420 fps for 6x6 sliding window size, 2.44 ms or 409 fps for 7x7, and 2.52 ms or 396 fps for 8x8.
立体视觉在机器人导航、高级驾驶支持系统、自动驾驶等领域有着广泛的应用。利用立体匹配实现立体视觉体系结构，得到视差图。一种立体匹配算法最近已经在FPGA上实现。本研究旨在利用Stratix V FPGA和OpenCL框架评估立体匹配。后者指的是通过提高代码的抽象来提高生产率的并行编程框架。此外，OpenCL允许使用通道扩展处理立体声匹配。在实验中，我们将OpenCL内核划分为三个较小的内核，在FPGA上检查立体匹配的计算。这种方法使FPGA全局存储器中的流图像像素成为可能。在将像素流到立体匹配内核内的窗口缓冲区时，使用行缓冲区来避免由于内存访问而导致的加载-存储依赖。我们可以通过使用通道扩展进行分区的OpenCL内核流式传输图像像素，从而实现快速的执行时间，这有利于实时实现。使用1242x375像素分辨率的立体KITTI数据集计算视差图的执行时间对于6x6滑动窗口大小达到2.38 ms或420 fps，对于7x7达到2.44 ms或409 fps，对于8x8达到2.52 ms或396 fps。
{"title":"FPGA-based acceleration of stereo matching using OpenCL","authors":"Iman Firmansyah, Y. Yamaguchi, Ryo Nakagawa","doi":"10.1145/3575882.3575883","DOIUrl":"https://doi.org/10.1145/3575882.3575883","url":null,"abstract":"Stereo vision finds a wide range of applications for robot navigation, advanced driving support system, and autonomous driving in the automotive industry. The disparity map can be obtained through the implementation of stereo vision architecture using stereo matching. A stereo matching algorithm has recently been executed in FPGA. This study is aimed at assessing the stereo matching with the use of Stratix V FPGA and OpenCL framework. The latter refers to a parallel programming framework that enhances productivity by raising the code’s abstraction. Additionally, OpenCL allows for the processing of stereo matching using channel extensions. In the experiment, we partitioned the OpenCL kernel into three smaller kernels to examine the stereo matching on FPGA for computation. Such an approach enables streaming image pixels from the FPGA global memory. A line-buffer is employed to avoid the load-store dependencies caused by memory accesses when streaming the pixels to the window buffer inside the stereo matching kernel. We can achieve a rapid execution time, which is advantageous for real-time implementation, by streaming the image pixels through an OpenCL kernel partitioned using channel extension. The execution time to compute the disparity map using the stereo KITTI dataset with 1242x375 pixels resolution reaches 2.38 ms or 420 fps for 6x6 sliding window size, 2.44 ms or 409 fps for 7x7, and 2.52 ms or 396 fps for 8x8.","PeriodicalId":367340,"journal":{"name":"Proceedings of the 2022 International Conference on Computer, Control, Informatics and Its Applications","volume":"79 4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121918479","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Turing's model is a model contains reaction-diffusion equation that capable to form skin patterns on an animal. In this paper, Turing's model was investigated, with the model improvisation by Barrio et al. [12], in parallel programming to shown its speed up impact. The parallel programming managed to speed up the process up to 8.9 times while retaining the quality of the result, compared to traditional programming.
{"title":"Parallel Programming in Finite Difference Method to Solve Turing's Model of Spot Pattern","authors":"Theodoret Putra Agatho, P. Pranowo","doi":"10.1145/3575882.3575910","DOIUrl":"https://doi.org/10.1145/3575882.3575910","url":null,"abstract":"Turing's model is a model contains reaction-diffusion equation that capable to form skin patterns on an animal. In this paper, Turing's model was investigated, with the model improvisation by Barrio et al. [12], in parallel programming to shown its speed up impact. The parallel programming managed to speed up the process up to 8.9 times while retaining the quality of the result, compared to traditional programming.","PeriodicalId":367340,"journal":{"name":"Proceedings of the 2022 International Conference on Computer, Control, Informatics and Its Applications","volume":"61 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114437005","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Customizing orders through the marketplace results in a very large number of product variants that must be made by manufacturers. Product customization that is too far from product standards can cause losses. So far, the manufacturer knows the loss when the order has been received and paid for by the consumer. The marketplace application cannot classify the types of orders that can or cannot be produced. Orders that have been received cannot be canceled by the manufacturer because it can lower the rating and credibility of the manufacturer. The use of machine learning in marketplace applications with random forest algorithms can classify order data, whether they can or cannot be produced. The results of the study prove that the rendom forest model made for order classification has accuracy=100%, sensitivity=100%, and specificity=100% for the dataset of batik shirt orders from consumers. Predictions are made based on order specifications, such as quantity, gender, size, collar type, cloth material, and sleeve type. The accuracy of the prediction results is also achieved by using the value of the number of trees (ntree) 50 with mtry 2. The dataset is in the form of order data as many as 3039 records taken within 6 weeks.
{"title":"Classification of Customer Orders in The Internal Section of Supply Chain Management Using Machine Learning","authors":"Wawa Wikusna, M. Mustafid, B. Warsito, A. Wibowo","doi":"10.1145/3575882.3575899","DOIUrl":"https://doi.org/10.1145/3575882.3575899","url":null,"abstract":"Customizing orders through the marketplace results in a very large number of product variants that must be made by manufacturers. Product customization that is too far from product standards can cause losses. So far, the manufacturer knows the loss when the order has been received and paid for by the consumer. The marketplace application cannot classify the types of orders that can or cannot be produced. Orders that have been received cannot be canceled by the manufacturer because it can lower the rating and credibility of the manufacturer. The use of machine learning in marketplace applications with random forest algorithms can classify order data, whether they can or cannot be produced. The results of the study prove that the rendom forest model made for order classification has accuracy=100%, sensitivity=100%, and specificity=100% for the dataset of batik shirt orders from consumers. Predictions are made based on order specifications, such as quantity, gender, size, collar type, cloth material, and sleeve type. The accuracy of the prediction results is also achieved by using the value of the number of trees (ntree) 50 with mtry 2. The dataset is in the form of order data as many as 3039 records taken within 6 weeks.","PeriodicalId":367340,"journal":{"name":"Proceedings of the 2022 International Conference on Computer, Control, Informatics and Its Applications","volume":"70 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129693723","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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